import torch
import torch.nn as nn
import math
from torchsummary import summary

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 用于Resnet18 Resnet34
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels * BasicBlock.expansion)

        self.residual = nn.Sequential()


        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.residual = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)

        out += self.residual(x)
        out = self.relu(out)
        return out

class BottleNeck(nn.Module):
    expansion = 4
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

        # 第二个卷积层：卷积核尺寸： 3×3 ，填充值为 1， 步长为 1
        self.conv2 = nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

        # 第三个卷积层：卷积核尺寸： 1×1 ，填充值为 0， 步长为 1
        self.conv3 = nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * BottleNeck.expansion)

        self.residual = nn.Sequential()

        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
            self.residual = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)

        out += self.residual(x)
        out = self.relu(out)
        return out

class ResNet(nn.Module):

    def __init__(self, block, num_block, num_classes=100):
        super().__init__()

        self.in_channels = 64

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True))

        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):

        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)
        output = self.conv4_x(output)
        output = self.conv5_x(output)
        output = self.avg_pool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output

if __name__ == "__main__":
    resnet18 = ResNet(BasicBlock, [2, 2, 2, 2]).to(device)
    resnet50 = ResNet(BottleNeck, [3, 4, 6, 3]).to(device)
    summary(resnet50, input_size=(3, 32, 32))
